Data-Driven Analysis of Irregular Respiratory Signals Derived from the Chest-Wearable Respeck Monitor

The respiratory rate and the respiratory flow/effort are important vital signs monitored during the care of patients in the hospital and during remote management of subjects with chronic lung diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and congenital airways malformation. The Respeck is a wireless device worn as a plaster on the chest which monitors continuously respiratory signals, such as the respiratory rate (breaths/minute), and the respiratory flow/effort (amplitude over time). The regularity of the respiratory signal, or rather the nature and frequency of occurrence of irregular breathing episodes can be used as one of the characteristics of the subjects' clinical status. This paper develops computational methods for classifying episodes of rapid-shallow breathing based on approximating the tidal volume using features such as: the area under the respiratory signal curve, peak respiratory flow and the respiratory rate. Results are presented for a data-driven approach to the analysis of a Respeck dataset of respiratory flow signals gathered from 137 asthmatic subjects.